NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Operational-ConditionIndependent Criteria Dedicated 

to Monitoring Wind Turbine 

Generators

Preprint

W. Yang

National Renewable Energy Centre

S. Sheng

National Renewable Energy Laboratory

R. Court

National Renewable Energy Centre

To be presented at the ASME 2012 International Mechanical 

Engineering Congress & Exposition

Houston, Texas

November 9−15, 2012

Conference Paper

NREL/CP-5000-55195

August 2012
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1 

OPERATIONAL-CONDITION-INDEPENDENT CRITERIA DEDICATED TO

MONITORING WIND TURBINE GENERATORS

Wenxian Yang

National Renewable Energy 

Centre (Narec)

Blyth, Northumberland, UK

Shuangwen Sheng

National Renewable Energy 

Laboratory (NREL)

Golden, Colorado, USA

Richard Court

National Renewable Energy 

Centre (Narec)

Blyth, Northumberland, UK

ABSTRACT

Condition monitoring is beneficial to the wind industry for 

both onshore and offshore plants. However, due to the 

variations in operational conditions, its potential has not been 

fully explored. There is a need to develop an operationalcondition-independent condition monitoring technique, which 

has motivated the research presented here. In this paper, three 

operational-condition-independent criteria are developed. The

criteria accomplish the condition monitoring by analyzing the 

wind turbine electrical signals in the time domain. Therefore, 

they are simple to calculate and ideal for online use. All 

proposed criteria were tested through both simulated and 

practical experiments. The experiments have shown that these 

criteria not only provide a solution for detecting both 

mechanical and electrical faults that occur in wind turbine 

generators, but provide a potential tool for diagnosing generator 

winding faults. 

INTRODUCTION

The wind industry continues to grow worldwide. In 

particular, offshore wind is attracting increasing interest 

because of its high and stable wind speed, and lack of

significant visual impact and noise issues. Take the offshore 

wind resource in the United Kingdom (UK) as an example. 

With 18 sites and a total electric-generating capacity of 1.5

gigawatts (GW), the UK government launched the first round 

of offshore wind farm development plans in 2000. To date, 11 

Round 1 offshore wind farms are fully operational, with a 

combined electric-generating capacity of 876 megawatts (MW). 

The 90-MW Teesside wind farm has been recently consented. 

Following Round 1, the government launched a plan for a 

second round of larger sites in 2003. In Round 2, 16 sites were 

awarded, totaling a combined capacity of up to 7.2 GW. Today, 

two Round 2 offshore wind farms (Gunfleet Sands II 64 MW 

and Thanet 300 MW) are fully operational, five are under 

construction, three were consented, and the other six are in 

planning. Subsequently, the UK offshore wind Round 3 scheme 

was announced in 2008, offering nine development zones and a 

total electric-generating capacity of 32 GW—equaling onefourth of the UK’s electricity needs. From these data, it can be 

inferred that large megawatt-scaled wind turbines are being 

increasingly deployed offshore. Moreover, the locations where 

the wind farms are deployed tend to increase in size and 

distance from shore. Hence, wind turbine operators are very 

concerned about the reliability and availability issues of these 

offshore giants. The early experience of UK Round 1 offshore 

wind farms [1] shows that although these farms achieve 

satisfactory capacity factors (about 29.5%), their annual 

average availability is only 80.2%. This is fairly low in 

comparison to the average availability of UK onshore wind 

farms (97%).

Apparently, the low availability has become a barrier to 

reducing the cost of energy from offshore wind. For example, 

the Barrow site, a standard UK Round 1 offshore wind farm, 

includes: 

• A rated power of 90 MW

• An annual average capacity factor of 29.5%

• A tariff rate of $117 (USD) per megawatt-hour 

(MWh).

The annual revenue of this wind farm can be estimated by

90MW × 365 days/year × 24 hours/day × 29.5% × 

117USD/MWh ≈ 27.2 million USD. Then it can be readily 

inferred that this wind farm will yield $272,000 USD more 

annual revenue if its production can be improved by 1%. For 

the future 32-GW UK Round 3 offshore wind development 

scheme, a 1% improvement in production could yield more

than 9.7 billion USD per year. Undoubtedly, this is a significant 

economic profit to operators. In practice, there are many 

methods that can be used to improve wind turbine availability. 

Specifically, condition monitoring (CM) has been proven as 

one of the most efficient methods [2, 3]. The additional value of 

a proper wind turbine condition-monitoring system (CMS) is 

observable from detecting the incipient faults occurring in wind 

turbines and their subassemblies, protecting the defective parts 

from second-damage, and preventing fatal, catastrophic wind 
2 

turbine accidents. The additional value can also be justified by 

the following actual wind turbine operation and maintenance 

(O&M) data. In the commercial market today, the component 

replacement costs for a 5-MW turbine include the following [3, 

4]: 

• For a rotor: 1.9−2.3 million USD 

• For a blade: 391−547 thousand USD 

• For a blade bearing: 62.5−78.2 thousand USD 

• For a gearbox: 628 thousand USD 

• For a generator: 314 thousand USD 

• For electronic modules: 16 thousand USD. 

By contrast, the current average market price of a CMS is only 

around $16,000 USD per unit. As a result, it may be worthwhile 

to equip offshore wind turbines with a CMS to ensure the 

turbine’s anticipated availability and economic return. 

However, wind practice shows that the wind turbine CMSs 

that are currently available in the market have not been as 

successful as expected at improving wind turbine availability. 

The reasons are complicated [2], but one of the major reasons is 

that the available CMSs are using the general-purpose CM 

techniques, which are unable to provide reliable CM results for 

wind turbines. Because wind turbines operate at variable 

rotational speeds and are constantly subjected to varying loads, 

these influences and the effects of potential faults are combined 

in wind turbine CM signals, which makes it difficult to interpret 

the signals and reduces the reliability of the CM results. 

Although there have been some recent efforts [5, 6], frequent 

false alarms generated by the wind turbine CMS still 

overwhelm the operators and make them hesitant to use CMS 

extensively on their machines. Therefore, it is necessary to 

remove the negative influences of varying loads and rotational 

speeds on wind turbine CM results in order to use CMS to 

improve wind turbine availability. Two straightforward 

approaches could be taken for a solution: 1) introduce 

operational-condition decoupling procedures into the current 

wind turbine CM techniques, and 2) develop new operationalcondition-independent wind turbine CM techniques. This paper 

demonstrates the second approach on monitoring wind turbine 

induction generators under continuously varying operational 

conditions. The details of this work follow. 

OPERATIONAL-CONDITION-INDEPENDENT 

CONDITION MONITORING CRITERIA 

To achieve a reliable CM technique that is dedicated for 

wind turbine induction generators that experience constantly 

varying operational conditions, the following criteria were 

researched. 

Criterion δ

For a wind turbine induction generator, neither varying 

load nor potential faults are able to influence the primary 

frequency of its stator current signal. But the sub- or higherorder of fault-related harmonics will distort the time waveform 

of the stator current signal and thus disturb its zero-crossing 

behavior. Whereas, the zero-crossing behavior of stator current 

signal is less dependent on load because varying load mainly 

affects the amplitude of the signal. Inspired by this knowledge, 

the first CM criterion, δ, was developed to describe the fault 

effect on the zero-crossing behavior of the stator current signal 

(see Figure 1). The figure shows the relative phase angles of the 

generator stator current signal with respect to the corresponding 

voltage signal in various scenarios. I1 represents the line current 

signal measured at phase 1 of generator stator, V1 represents the 

corresponding phase voltage signal, and Δt represents the 

leading/lagging time of I1, with respect to V1. 

Figure 1: Zero-crossing behavior of the stator current signal in 

different scenarios 

As Figure 1 shows, the potential generator winding faults 

will either increase or decrease the value of Δt , thus disturbing 

the zero-crossing behavior of its stator current signal. So 

potentially Δt can be an indicator of the health of a wind turbine 

induction generator. However, Δt is not accurate enough to be a 

CM criterion. It only reflects the shift of the current signal, 

however, it does not exactly show how much the phase shift is 

with respect to the corresponding voltage signal. In other 

words, the same value of Δt might indicate a different level of 

phase shift of the current signal when the grid frequency is 

different [either 50 hertz (Hz) or 60 Hz, depending on the 

areas]. Therefore, an improvement of Δt occurs by normalizing 

Δt using the frequency of grid. Hence, a new CM criterion, δ, is 

designed as: 

ൌ ߜ

ݐ∆

ܶ ൈ 360௢ (1) 

Where 

ܶ ൌ

1

݂௚௥௜ௗ

(2) 

݂௚௥௜ௗ refers to grid frequency. 

From a physics point of view, ߜ is less dependent on the 

external load applied to the generator because it is only related 

to the impedance status and the magnetizing behavior of the 

generator. However, the potential faults occurring in an 

induction generator will disturb its circuit impedance or ability 

to magnetize. In other words: 
3 

• Either an inter-turn or open-circuit winding fault occurring 

in the generator rotor or stator will change the impedance 

of the generator circuit, thus leading to the change of 

 ߜ criterion

• The generator mechanical faults, usually related to rotor 

shaft, bearings, and air-gap eccentricity, will alter the 

rotor-to-stator air gap statically or dynamically. Then, the 

modified air gap will result in the change of the 

magnetizing current of the generator. As a consequence, 

criterion ߜ will change correspondingly. 

Therefore, criterion ߜ should be able to send an alert of 

both electrical and mechanical faults occurring in the generator. 

Criterion r 

As mentioned above, both varying load and generator 

abnormalities will influence the power (and power quality) 

output from a wind turbine induction generator. The difference 

is that varying load can alter the power but not the physical 

performance of the generator, however, the generator 

abnormalities can alter both. For example, a generator winding 

fault can change the circuit impedance of an induction 

generator, whereas an external load cannot. So the circuit 

impedance of an induction generator is load-independent. 

However, it can be a challenge to accurately measure generator 

circuit impedance online. In addition, there is a lack of control 

over uncertainties related to slip rings or carbon brushes. The 

metering accuracy and sensitivity of the data acquisition 

instruments are also concerns. In response to these issues, an 

alternative criterion, ݎ, was developed for the purpose of 

indicating the status of the circuit impedance of an induction 

generator. It is based on a statistical calculation of the generator 

stator line current and the corresponding voltage signals. The 

criterion ݎሺݐሻ, expressed in terms of line current ܫଵሺݐሻ and the 

corresponding phase voltage ܸଵሺݐሻ, can be written as: 

ݎሺݐሻ ൌ ඩ

∑ ሾܸଵሺݐ ൅ ݐߜሻሿ ఛ ଶ ଶ ⁄

ఋ௧ୀିఛ ଶ⁄

∑ ሾܫଵሺݐ ൅ ݐߜሻሿ ఛ ଶ ଶ ⁄

ఋ௧ୀିఛ ଶ⁄

(3) 

where, a sliding window with the width of τ is used in the 

calculation. Subscript ‘1’ indicates the phase 1 of the generator 

stator. The criterion can also be assessed by using the electrical 

signals measured from phase 2 or phase 3 of the generator 

stator. 

Inter-turn or open-circuit faults that occur in the winding of 

the generator rotor or stator will change the circuit impedance 

of the generator. Therefore, researchers believe that this change 

should be observable from criterion ݎ. The generator shaft and 

bearing-related faults will statically or dynamically alter the 

rotor-to-stator eccentricity, which will affect the magnetizing 

current of the generator. As a result, criterion ݎ will change as 

well. 

In principle, criterion ݎ is load-independent and valid for 

detecting both electrical and mechanical faults that occur in 

wind turbine induction generators. 

Criterion e 

Park’s vector [7] successfully describes the three-phase 

phenomenon in a two-dimensional coordinate system that 

allows the pattern-based CM of three-phase induction motors 

[8]. This technique can be applied to the CM of three-phase 

induction generators. The current Park’s vector components 

൫݅ௗ, ݅௤൯ can be calculated by: 

ە

ۖ

۔

ۖ

ۓ

݅ௗ ൌ ඨ2

3 ܫଵ െ 1

√6

ܫଶ െ 1

√6

ଷܫ

݅௤ ൌ 1

√2

ܫଶ െ 1

√2

 ଷܫ

(4) 

where ܫଵ, ܫଶ, and ܫଷ represent the line currents measured from 

the stator of a three-phase generator/motor. 

When the health condition of the generator is without any 

defect, ൫݅ௗ, ݅௤൯ is characterized by a circle pattern that is 

centered at the origin of the coordinate system. The size of the 

circle is related to the power generated by the generator. For 

example, the larger the power, the bigger the circle. Apparently, 

the pattern depicted by ൫݅ௗ, ݅௤൯ is load-dependent. So if ݅ௗ and 

݅௤ are applied directly to CM application (as done in [8]), it is 

hard to get a creditable CM result under a varying loading 

condition. To help alleviate this issue, a further improvement is 

provided below. 

Because the external load will change the values of ܫଵ, ܫଶ, 

and ܫଷ, it will affect the values of ݅ௗ and ݅௤ and the size of the 

pattern depicted by ൫݅ௗ, ݅௤൯. However, external load is unable to 

change the eccentricity and roughness of the pattern curve, 

which are only dependent on the balance status of the generator 

load (i.e., the balance of three phases) and the harmonics (either 

sub- or higher order) that are induced by the faults. Therefore, 

the pattern eccentricity and the roughness of its curve are loadindependent and have the potential to be used for generator 

CM. To provide a comprehensive description of the pattern 

eccentricity and curve roughness, a new CM criterion, ݁, is 

developed: 

݁ሺݐሻ ൌ ܴ௠௔௫ሺݐሻ െ ܴ௠௜௡ሺݐሻ

ܴതሺݐሻ ൈ 100% (5) 

Where 

ܴതሺݐሻ ൌ 1

ܶ න ܴሺ߬ሻ݀߬ ௧ା் ଶ⁄

௧ି் ଶ⁄

(6) 

ܴሺ߬ሻ ൌ ห݅ௗሺ߬ሻ ൅ ݆ ൈ ݅௤ሺ߬ሻห (7) 

ܴ௠௔௫ሺݐሻ ൌ ݉ܽݔሺܴሺ߬ሻሻ, ߬ א ሾݐെܶ 2⁄ ,ݐ൅ܶ 2⁄ ሿ (8) 

ܴ௠௜௡ሺݐሻ ൌ ݉݅݊ሺܴሺ߬ሻሻ,߬ א ሾݐെܶ 2⁄ ,ݐ൅ܶ 2⁄ ሿ (9) 

ܶ stands for the frequency period of grid. 

FAULT SIMULATIONS 

To verify the CM criteria, both inter-turn and open-circuit 

faults are simulated on the rotor and stator windings of an 

induction generator by using Simulink. The winding fault 

simulation model is shown in Figure 2, and the parameters of 
4 

the generator being simulated are listed in Table 1. In the 

experiment, the winding faults are simulated by introducing 

electrical asymmetry into the generator rotor and stator circuit, 

and the CM signals (i.e., line currents, phase voltages, and total 

power) are measured from the stator of the generator. 

Figure 2: Model for simulating generator winding faults 

In Figure 2, ܮ refers to the inductance and ܴଵ refers to the 

resistance of the transmission line. ܴଶ ൌ1e3Ω represents the 

phase resistance to the ground. In the model, resistances ܴଷ and 

ܴସ are specially designed to introduce electrical asymmetries 

into the stator and rotor of the generator. The initial values are 

ܴଷ ൌ1.16e-3Ω and ܴସ ൌ3.92e-2Ω in the absence of electrical 

asymmetry. 

Parameter Value Parameter Value 

Rated power, ܵ௕ 2 MW Rotor leakage 

௥ܮ ,inductance

כ 1.493e-1 p.u. 

Line-line voltage, 

ܸ௡

690 volts (V) Magnetizing 

௠ܮ ,inductance

כ 3.9527 p.u. 

Rated frequency, 

݂௕

50 Hz Winding connection 

(stator/rotor) Y-Y 

Stator resistance, 

ܴ௦

כ 4.88e-3 p.u. Turns ratio (ܰ௦⁄ܰ௥) 0.45 

Stator leakage 

inductance, ܮ௦

כ 1.386e-1 p.u. Inertia 32 Kg·m2

Rotor resistance, 

ܴ௥

כ 5.49e-3 p.u. Friction 2.985e-4 

N·m·s 

Table 1: The parameters used for simulating an induction 

generator [9]. 

Based on the parameters listed in Table 1, the transmission 

line impedance can be estimated as: 

ܴଵ ൅ ݆ܮ ൌ 0.01ܼ௦௕ ൅ ݆ ൈ 0.1ܮ௦௕ (10) 

where 

ܼ௦௕ ൌ ܸ௦௕

௕௦ܫ

(11) 

ܸ௦௕ ൌ ඨ2

3 ܸ௡ (12) 

ܫ௦௕ ൌ ඨ2

3

ൈ

ܵ௕

ܸ௡

(13) 

ܮ௦௕ ൌ ܼ௦௕

௕݂ߨ2

(14) 

By submitting (11)-(14) into (10), obtain ܴଵ ൌ2.3805e-3Ω and 

ܮ ൌ7.6e-5H. 

Winding faults in rotor

The inter-turn and open-circuit faults are simulated first in 

the generator rotor by setting the resistance in the second phase 

to R4=3.92e-3Ω and R4=3.92e-1Ω. The resulting electrical 

signals and the corresponding CM results are shown in Figure 

3. 

(a) Electrical signals 

(b) CM results

Figure 3: Simulation and CM of generator rotor winding faults 

Figure 3(a) shows that both the inter-turn and open-circuit 

faults that occur in the rotor significantly distort the timewaveforms of stator current signals, but have less of an effect 

on the time-waveforms of stator voltage signals. This is because 

the generator voltage is mainly dominated by the grid’s voltage, 

whereas the generator stator current is a different matter. The 

generator stator current is not only dependent on the generator’s 

external load and operational condition, but on its actual health 

status. Researchers also noticed that the total power signal 

appears in direct current (DC) form when the generator is 

healthy and without any winding defect, and appears in 

alternating current (AC) form time-waveforms in the presence 

of either inter-turn or open-circuit winding faults. In addition, 

researchers found that the power signals that were collected in 

two fault scenarios show distinctly different pattern features, 

for example, the power fluctuates slowly along time in the 

presence of an inter-turn fault, but fluctuates quickly in the 

presence of an open-circuit fault. The similar phenomena can 

-5

0

5

currents (kA)

-1

0

1

voltages (kV)

0 0.5 1 1.5

0

2

4

power (MW)

time (s)

normal inter-turn open circuit

150

200

250

300

δ (degree)

0

0.005

0.01

r

0 0.5 1 1.5

0

50

100

150

e (%)

time (s)

normal inter-turn open circuit
5 

also be observed from the time-waveforms of the current 

signals. The two types of rotor winding faults have different 

characteristic frequencies in generator electrical signals. This 

could be important information, not only to the generator’s CM 

but also to its fault diagnosis. 

Figure 3(b) shows that all three proposed CM criteria are 

aware of the presence of both types of rotor winding faults, 

although the faults display different variation characteristics in 

different scenarios. The CM results shown in Figure 3b prove 

that the winding faults that occur in the generator rotor will: 

• Disturb the zero-crossing behavior of generator stator 

current signal (characterized by criterion δ) 

• Change the circuit impedance of the generator 

(characterized by criterion ݎ (

• Cause load imbalance to the generator (characterized 

by criterion ݁). 

Winding faults in stator

The inter-turn and open-circuit faults are simulated in the 

stator of the generator by setting the resistance in its second 

phase to R3=1.16e-4Ω and R3=1.16e-2Ω. The electrical signals 

and corresponding calculation results of CM criteria are shown 

in Figure 4. 

(a) Electrical signals 

(b) CM results

Figure 4: Simulation and CM of generator stator 

winding faults 

Figure 4(a) shows that, compared to the winding faults that 

occur in the generator rotor, both inter-turn and open-circuit 

faults that occur in the generator stator do not have a significant 

effect on the time-waveforms of stator current and voltage 

signals. However, like a generator rotor winding fault, they 

create a load imbalance in the generator, which is characterized 

by the AC form of power signal. 

Figure 4(b) shows that all three proposed CM criteria are 

equally sensitive to both types of stator winding faults. For 

example: 

• An inter-turn circuit fault decreases the value of 

criterion δ, while an open-circuit fault increases it. 

This finding further supports the phase shift 

phenomena of the current signal shown in Figure 1. 

• An inter-turn circuit fault decreases the circuit 

impedance of the generator, while an open-circuit fault 

increases it. 

• Both inter-turn and open-circuit faults change the 

pattern depicted by Park’s vector ൫݅ௗ, ݅௤൯, thus 

changing criterion ݁. 

VERIFICATION EXPERIMENTS 

The verification experiments were conducted on a 30-

kilowatt (kW), three-phase, four-pole induction generator, 

which was driven by a 54-kW, DC-variable speed motor 

through a two-stage gearbox with a gear ratio of 1:5. To 

emulate faults, the rotor circuit of the generator was coupled to 

an externally connected, three-phase resistive load bank (see 

Figure 5). As a result, electrical asymmetry could be readily 

applied to the generator rotor by adjusting the phase resistances 

of the load bank. The test rig was instrumented and controlled 

using LabVIEW, allowing researchers to apply a variety of 

wind speed inputs to the test rig via the DC motor. The 

rotational speed of the DC motor was controlled by a 

mathematical model that incorporates the properties of natural 

wind at different wind speeds and the mechanical behavior of a 

2-MW wind turbine operating under closed-loop control 

conditions. More details about this test rig are provided in [10]. 

In the experiment, both fixed and variable rotational speeds 

were applied to the DC motor and generator to verify the 

capabilities of the three proposed CM criteria under various 

loading conditions. 

Researchers verified and applied the CM criteria when 

monitoring the generator at a fixed rotational speed of 1,750 

revolutions per minute (rpm). In addition, researchers 

periodically applied a winding fault to the generator’s rotor by 

repeatedly adjusting the phase resistances of the load bank. The 

electrical signals were collected from the generator terminals 

by using a sampling frequency of 5 kilohertz (kHz). The timewaveforms of the electrical signals and the corresponding CM 

results are shown in Figure 6. In the figure, normal represents 

electrical symmetry and abnormal represents electrical 

asymmetry. 

-5

0

5

currents (kA) -1

0

1

voltages (kV)

0 0.5 1 1.5 1.8

2

2.2

power (MW)

time (s)

normal inter-turn open circuit

209

210

211

212

213

δ (degree) 4.5

5

5.5

6 x 10-4

r

0 0.5 1 1.5 0

2

4

6

8

e (%)

time (s)

normal inter-turn open circuit
6 

Figure 5: Test rig for conducting verification experiments 

(Source: Narec) 

(a) Electrical signals 

(b) CM results

Figure 6: CM when generator rotates at constant 

rotational speed 

Compared to Figure 3(a), the emulated rotor winding fault 

shown in Figure 6(a) does not disturb the time-waveform of the 

stator current signal as anticipated. This is because the 

asymmetry that is introduced by the load bank is too small to be 

observable from the measured current signal. To confirm this, 

the current signal shown in Figure 6(a) was amplified 40 times. 

The amplified time-waveform of the current signal is shown in 

Figure 7, from which the waveform distortion that was caused 

by the asymmetry has already been clearly visible. So it is 

demonstrated, once again, that the winding fault that occurs in 

the induction generator’s rotor distorts the time-waveform of its 

stator current signal. Compared to the effect of the winding 

fault on generator stator current signal, the effect of the 

distortion on generator total power is much more significant. In 

Figure 6(a), a big distortion of generator power signal happens 

as soon as the fault occurs, and the power returns back to its 

normal waveform immediately when the fault goes away. This 

suggests that, in comparison to the stator current signal, the 

generator total power signal is more alert to the fault. This is 

why the generator power signal, rather than the stator current 

signal, was adopted in [6] for wind turbine condition 

monitoring, even though the majority of existing 

motor/generator CM techniques are based on the analysis of 

stator current signals [11]. 

Figure 7: Amplified time-waveform of the stator current signal 

In Figure 6(b), the winding fault has been successfully 

detected by all three proposed CM criteria, although these 

criteria are developed based on the analysis of the stator current 

and voltage signals. 

To further verify the CM capability of the proposed criteria 

under varying loading conditions, as observed from real wind 

turbines, researchers repeated the experiment when the DC 

motor was controlled to operate at constantly varying speeds. 

As part of this process, the electrical signals (i.e., stator current, 

voltage, and total power) were collected from the generator 

terminals by using the same sampling frequency of 5 kHz. The 

time-waveforms of these CM signals and the corresponding 

results are shown in Figure 8. 

Figure 8(a) shows that, not only is the winding fault 

undetected by the time-waveforms of the stator current and 

voltage signals, but it is undetected by the generator power 

signal, due to the negative influence of varying loads. 

Nonetheless, the fault is detected successfully by using the 

three proposed CM criteria [see Figure 8(b)]. However, the 

result of criterion δ is not completely load-independent, 

therefore a de-trending process is necessary. 

(a) Electrical signals 

-0.05

0

0.05

currents (kA)

-0.5

0

0.5

voltages (kV)

0 20 40 60 80 100 -3

-2

-1

0

power (KW)

time (s)

normal abnormal normal abnormal normal

82

84

86

88

δ (degree)

0

0.01

0.02

r

0 20 40 60 80 100 4

6

8

e (%)

time (s)

normal abnormal normal abnormal normal

0 20 40 60 80 100

-2

0

2

currents (kA)

time (s)

normal abnormal normal abnormal normal

-0.05

0

0.05

currents (kA)

-0.5

0

0.5

voltages (kV)

0 20 40 60 80 100 -4

-2

0

2

power (KW)

time (s)

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7 

(b) CM results

Figure 8: CM when the generator rotates at a varying rotational 

speed 

CONCLUSIONS 

In this paper, researchers first discussed the influences of 

various types of winding faults on an induction generator. Three 

operational-condition-independent condition monitoring (CM) 

criteria were researched to achieve a reliable CM of a wind 

turbine generator that is usually subject to constantly varying 

loads. Based on this work, researchers came to the following 

conclusions: 

• Winding faults that occur in a generator rotor will distort 

the time-waveform of the generator stator current signal, 

but the faults have less influence on the stator voltage 

signal. 

• Compared to the stator current and voltage signals, the 

generator total power signal is more alert to the presence 

of generator faults. 

• All three proposed CM criteria are able to successfully 

detect the generator winding faults, despite the loading 

conditions. However, experimental results indicate that 

criterion δ is not completely load-independent. Under 

varying loading conditions, an additional de-trending of 

the criterion could be necessary to further improve its fault 

detection capability. 

• Experimental results suggest that different types of rotor 

winding faults show different characteristics in the stator 

current signal. This is potentially important information to 

both CM and fault diagnosis of the induction generator. 

ACKNOWLEDGMENTS 

The work described above was supported by the United 

Kingdom National Renewable Energy Centre (Narec) and 

China Natural Science Foundation, with project reference 

number 51075331. 

REFERENCES 

[1] Feng, Y., Tavner, P., Long, H., 2010, “Early experiences 

with UK Round 1 offshore wind farms,” Proceedings of the 

Institution of Civil Engineers: Energy, 163(4), 167−181. 

[2] Yang, Wenxian, Tavner, P., Crabtree, C., Feng, Y., Qiu, Y., 

2012, “Wind turbine condition monitoring: Technical and 

commercial challenges,” Wind Energy, In Press. 

[3] McMillan, D., Ault, G.W., 2007, “Quantification of 

condition monitoring benefit for offshore wind turbines,” Wind 

Engineering, 31(4), 267−285. 

[4] BVG Associates, 2011, “A guide to an offshore wind farm.” 

[5] Yang, Wenxian, Court, R., Tavner, P., Crabtree, C., 2011, 

“Bivariate empirical mode decomposition and its contribution 

to wind turbine condition monitoring,” Journal of Sound and 

Vibration, 330(15), 3766−3782. 

[6] Yang, Wenxian, Tavner, P., Crabtree, C., Wilkinson, M.R., 

2010, “Cost-effective condition monitoring for wind turbines,” 

IEEE Transactions on Industrial Electronics, 57(1), 263−271. 

[7] Cardoso, A.J.M., Saraiva, E.S., 1993, “Computer-aided 

detection of airgap eccentricity in operating three-phase 

induction motors by Park’s vector approach,” IEEE 

Transactions on Industry Applications, 29(5), 897−901. 

[8] Nejjari, H., Benbouzid, M.E.H., 2000, “Monitoring and 

diagnosis of induction motors electrical faults using a current 

Park’s vector pattern learning approach,” IEEE Transactions on 

Industry Applications, 36(3), 730−735. 

[9] Xiang, D., Ran, L., Tavner, P., Yang, S., 2006, “Control of 

doubly fed induction generator in a wind turbine during grid 

fault ride-through,” IEEE Transactions on Energy Conversion, 

21(3), 652−662. 

[10] Yang, Wenxian, Tavner, P., Court, R., “An online technique 

for condition monitoring the induction generators used in wind 

and marine turbines,” Mechanical Systems and Signal 

Processing, In Press. 

[11] Tavner, P., 2008, “Review of condition monitoring of 

rotating electrical machines,” IET Electric Power Applications, 

2(4), 215−247. 

80

85

90

δ (degree)

0

0.01

0.02

r

0 20 40 60 80 100

4

6

8

e (%)

time (s)

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